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Med-RewardBench – AI Model by Nandzy | AlphaNeural AI
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Med-RewardBench
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Med-RewardBench Inference and Evaluation
Data
Please download the dataset from
Nandzy/Med-Reward
.
Inference
We recommend using
vLLM
as the inference engine for optimal performance. Once you have deployed your model using vLLM (ensuring the port is set to
8000
), you can use
4_api_benchmark_multi.py
to run inference on the dataset.
python 4_api_benchmark_multi.py
--model "Qwen/Qwen2-VL-7B-Instruct"
Evaluation
To assess the model's performance, use
5_calculate_accuracy.py
. Update the script with the correct data path corresponding to the output file.
python 5_calculate_accuracy.py --output_file "reaults.txt"
This will compute relevant metrics and provide insights into the model's effectiveness.